Abstract
The aim of this paper is to investigate the feasibility of predicting the gender of a text document’s author using linguistic evidence. For this purpose, term- and style-based classification techniques are evaluated over a large collection of chat messages. Prediction accuracies up to 84.2% are achieved, illustrating the applicability of these techniques to gender prediction. Moreover, the reverse problem is exploited, and the effect of gender on the writing style is discussed.
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Kucukyilmaz, T., Cambazoglu, B.B., Aykanat, C., Can, F. (2006). Chat Mining for Gender Prediction. In: Yakhno, T., Neuhold, E.J. (eds) Advances in Information Systems. ADVIS 2006. Lecture Notes in Computer Science, vol 4243. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11890393_29
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DOI: https://doi.org/10.1007/11890393_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46291-0
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